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        numpy ::
        ndarray ::
        Class&nbsp;ndarray
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<!-- ==================== CLASS DESCRIPTION ==================== -->
<h1 class="epydoc">Class ndarray</h1><p class="nomargin-top"></p>
<pre class="base-tree">
object --+
         |
        <strong class="uidshort">ndarray</strong>
</pre>

<dl><dt>Known Subclasses:</dt>
<dd>
      <ul class="subclass-list">
<li>ma.core.MaskedArray</li>  </ul>
</dd></dl>

<hr />
<pre class="literalblock">
ndarray(shape, dtype=float, buffer=None, offset=0,
        strides=None, order=None)

An array object represents a multidimensional, homogeneous array
of fixed-size items.  An associated data-type object
describes the format of each element in the array (its byte-order,
how many bytes it occupies in memory, whether it is an integer or
a floating point number, etc.).

Arrays should be constructed using `array`, `zeros` or `empty` (refer to
the ``See Also`` section below).  The parameters given here describe
a low-level method for instantiating an array (`ndarray(...)`).

For more information, refer to the `numpy` module and examine the
the methods and attributes of an array.

Attributes
----------
T : ndarray
    Transponent of the array.
data : buffer
    Array data in memory.
dtype : data type
    Data type, describing the format of the elements in the array.
flags : dict
    Dictionary containing information related to memory use, e.g.,
    'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', and others.
flat : ndarray
    Return flattened version of the array as an iterator.  The iterator
    allows assignments, e.g., ``x.flat = 3``.
imag : ndarray
    Imaginary part of the array.
real : ndarray
    Real part of the array.
size : int
    Number of elements in the array.
itemsize : int
    The size of each element in memory (in bytes).
nbytes : int
    The total number of bytes required to store the array data,
    i.e., ``itemsize * size``.
ndim : int
    The number of dimensions that the array has.
shape : tuple of ints
    Shape of the array.
strides : tuple of ints
    The step-size required to move from one element to the next in memory.
    For example, a contiguous ``(3, 4)`` array of type ``int16`` in C-order
    has strides ``(8, 2)``.  This implies that to move from element to
    element in memory requires jumps of 2 bytes.  To move from row-to-row,
    one needs to jump 6 bytes at a time (``2 * 4``).
ctypes : ctypes object
    Class containing properties of the array needed for interaction
    with ctypes.
base : ndarray
    If the array is a view on another array, that array is
    its `base` (unless that array is also a view).  The `base` array
    is where the array data is ultimately stored.

Parameters
----------
shape : tuple of ints
    Shape of created array.
dtype : data type, optional
    Any object that can be interpreted a numpy data type.
buffer : object exposing buffer interface, optional
    Used to fill the array with data.
offset : int, optional
    Offset of array data in buffer.
strides : tuple of ints, optional
    Strides of data in memory.
order : {'C', 'F'}, optional
    Row-major or column-major order.

See Also
--------
array : Construct an array.
zeros : Create an array and fill its allocated memory with zeros.
empty : Create an array, but leave its allocated memory unchanged.
dtype : Create a data type.

Notes
-----
There are two modes of creating an array using __new__:

1. If `buffer` is None, then only `shape`, `dtype`, and `order`
   are used.
2. If `buffer` is an object exporting the buffer interface, then
   all keywords are interpreted.

No __init__ method is needed because the array is fully initialized
after the __new__ method.

Examples
--------
These examples illustrate the low-level `ndarray` constructor.  Refer
to the `See Also` section for easier ways of constructing an ndarray.

First mode, `buffer` is None:

&gt;&gt;&gt; np.ndarray(shape=(2,2), dtype=float, order='F')
array([[ -1.13698227e+002,   4.25087011e-303],
       [  2.88528414e-306,   3.27025015e-309]])

Second mode:

&gt;&gt;&gt; np.ndarray((2,), buffer=np.array([1,2,3]),
...            offset=np.int_().itemsize,
...            dtype=int) # offset = 1*itemsize, i.e. skip first element
array([2, 3])

</pre>

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Object of same type as a from ndarray obj.

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      y in x</td>
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Deep copy of array

</pre></span>
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    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__index__"></a><span class="summary-sig-name">__index__</span>(<span class="summary-sig-arg">...</span>)</span><br />
      x[y:z] &lt;==&gt; x[y.__index__():z.__index__()]</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
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    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__int__"></a><span class="summary-sig-name">__int__</span>(<span class="summary-sig-arg">x</span>)</span><br />
      int(x)</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__invert__"></a><span class="summary-sig-name">__invert__</span>(<span class="summary-sig-arg">x</span>)</span><br />
      ~x</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
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<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__ior__"></a><span class="summary-sig-name">__ior__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x|y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__ipow__"></a><span class="summary-sig-name">__ipow__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x**y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__irshift__"></a><span class="summary-sig-name">__irshift__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x&gt;&gt;y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__isub__"></a><span class="summary-sig-name">__isub__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x-y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__iter__"></a><span class="summary-sig-name">__iter__</span>(<span class="summary-sig-arg">x</span>)</span><br />
      iter(x)</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__itruediv__"></a><span class="summary-sig-name">__itruediv__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x/y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__ixor__"></a><span class="summary-sig-name">__ixor__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x^y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__le__"></a><span class="summary-sig-name">__le__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x&lt;=y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__len__"></a><span class="summary-sig-name">__len__</span>(<span class="summary-sig-arg">x</span>)</span><br />
      len(x)</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__long__"></a><span class="summary-sig-name">__long__</span>(<span class="summary-sig-arg">x</span>)</span><br />
      long(x)</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__lshift__"></a><span class="summary-sig-name">__lshift__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x&lt;&lt;y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__lt__"></a><span class="summary-sig-name">__lt__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x&lt;y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__mod__"></a><span class="summary-sig-name">__mod__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x%y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__mul__"></a><span class="summary-sig-name">__mul__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x*y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__ne__"></a><span class="summary-sig-name">__ne__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x!=y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__neg__"></a><span class="summary-sig-name">__neg__</span>(<span class="summary-sig-arg">x</span>)</span><br />
      -x</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type"><pre class="literalblock">
a new object with type S, a subtype of T

</pre></span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#__new__" class="summary-sig-name">__new__</a>(<span class="summary-sig-arg">T</span>,
        <span class="summary-sig-arg">S</span>,
        <span class="summary-sig-arg">...</span>)</span></td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__nonzero__"></a><span class="summary-sig-name">__nonzero__</span>(<span class="summary-sig-arg">x</span>)</span><br />
      x != 0</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__oct__"></a><span class="summary-sig-name">__oct__</span>(<span class="summary-sig-arg">x</span>)</span><br />
      oct(x)</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__or__"></a><span class="summary-sig-name">__or__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x|y</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__pos__"></a><span class="summary-sig-name">__pos__</span>(<span class="summary-sig-arg">x</span>)</span><br />
      +x</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__pow__"></a><span class="summary-sig-name">__pow__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>,
        <span class="summary-sig-arg">z</span>=<span class="summary-sig-default">...</span>)</span><br />
      pow(x, y[, z])</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__radd__"></a><span class="summary-sig-name">__radd__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      y+x</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rand__"></a><span class="summary-sig-name">__rand__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      y&amp;x</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rdiv__"></a><span class="summary-sig-name">__rdiv__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      y/x</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rdivmod__"></a><span class="summary-sig-name">__rdivmod__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      divmod(y, x)</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#__reduce__" class="summary-sig-name">__reduce__</a>(<span class="summary-sig-arg">a</span>)</span><br />
      For pickling.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#__repr__" class="summary-sig-name">__repr__</a>(<span class="summary-sig-arg">x</span>)</span><br />
      repr(x)</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rfloordiv__"></a><span class="summary-sig-name">__rfloordiv__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      y//x</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rlshift__"></a><span class="summary-sig-name">__rlshift__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      y&lt;&lt;x</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rmod__"></a><span class="summary-sig-name">__rmod__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      y%x</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rmul__"></a><span class="summary-sig-name">__rmul__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      y*x</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__ror__"></a><span class="summary-sig-name">__ror__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      y|x</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rpow__"></a><span class="summary-sig-name">__rpow__</span>(<span class="summary-sig-arg">y</span>,
        <span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">z</span>=<span class="summary-sig-default">...</span>)</span><br />
      pow(x, y[, z])</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rrshift__"></a><span class="summary-sig-name">__rrshift__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      y&gt;&gt;x</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rshift__"></a><span class="summary-sig-name">__rshift__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x&gt;&gt;y</td>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rsub__"></a><span class="summary-sig-name">__rsub__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      y-x</td>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rtruediv__"></a><span class="summary-sig-name">__rtruediv__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      y/x</td>
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<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__rxor__"></a><span class="summary-sig-name">__rxor__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      y^x</td>
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    </td>
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<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__setitem__"></a><span class="summary-sig-name">__setitem__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">i</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x[i]=y</td>
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<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#__setslice__" class="summary-sig-name">__setslice__</a>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">i</span>,
        <span class="summary-sig-arg">j</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x[i:j]=y</td>
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<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#__setstate__" class="summary-sig-name">__setstate__</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">version</span>,
        <span class="summary-sig-arg">shape</span>,
        <span class="summary-sig-arg">dtype</span>,
        <span class="summary-sig-arg">isfortran</span>,
        <span class="summary-sig-arg">rawdata</span>)</span><br />
      For unpickling.</td>
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    </td>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
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          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#__str__" class="summary-sig-name">__str__</a>(<span class="summary-sig-arg">x</span>)</span><br />
      str(x)</td>
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    </td>
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<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__sub__"></a><span class="summary-sig-name">__sub__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x-y</td>
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    </td>
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<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__truediv__"></a><span class="summary-sig-name">__truediv__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x/y</td>
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          </td>
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      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="__xor__"></a><span class="summary-sig-name">__xor__</span>(<span class="summary-sig-arg">x</span>,
        <span class="summary-sig-arg">y</span>)</span><br />
      x^y</td>
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    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#all" class="summary-sig-name">all</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Returns True if all elements evaluate to True.</td>
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<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#any" class="summary-sig-name">any</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Check if any of the elements of `a` are true.</td>
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<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#argmax" class="summary-sig-name">argmax</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Return indices of the maximum values along the given axis of `a`.</td>
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<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#argmin" class="summary-sig-name">argmin</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Return indices of the minimum values along the given axis of `a`.</td>
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          </td>
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    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#argsort" class="summary-sig-name">argsort</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">-1</span>,
        <span class="summary-sig-arg">kind</span>=<span class="summary-sig-default">'quicksort'</span>,
        <span class="summary-sig-arg">order</span>=<span class="summary-sig-default">None</span>)</span><br />
      Returns the indices that would sort this array.</td>
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    </td>
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<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#astype" class="summary-sig-name">astype</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">t</span>)</span><br />
      Copy of the array, cast to a specified type.</td>
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    </td>
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<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#byteswap" class="summary-sig-name">byteswap</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">inplace</span>)</span><br />
      Swap the bytes of the array elements</td>
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        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#choose" class="summary-sig-name">choose</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">choices</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">mode</span>=<span class="summary-sig-default">'raise'</span>)</span><br />
      Use an index array to construct a new array from a set of choices.</td>
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    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#clip" class="summary-sig-name">clip</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">a_min</span>,
        <span class="summary-sig-arg">a_max</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Return an array whose values are limited to ``[a_min, a_max]``.</td>
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    </td>
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<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#compress" class="summary-sig-name">compress</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">condition</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Return selected slices of this array along given axis.</td>
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      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="conj"></a><span class="summary-sig-name">conj</span>(<span class="summary-sig-arg">a</span>)</span><br />
      Return an array with all complex-valued elements conjugated.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="conjugate"></a><span class="summary-sig-name">conjugate</span>(<span class="summary-sig-arg">a</span>)</span><br />
      Return an array with all complex-valued elements conjugated.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#copy" class="summary-sig-name">copy</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">order</span>=<span class="summary-sig-default">'C'</span>)</span><br />
      Return a copy of the array.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#cumprod" class="summary-sig-name">cumprod</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">dtype</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Return the cumulative product of the elements along the given axis.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#cumsum" class="summary-sig-name">cumsum</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">dtype</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Return the cumulative sum of the elements along the given axis.</td>
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        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#diagonal" class="summary-sig-name">diagonal</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">offset</span>=<span class="summary-sig-default">0</span>,
        <span class="summary-sig-arg">axis1</span>=<span class="summary-sig-default">0</span>,
        <span class="summary-sig-arg">axis2</span>=<span class="summary-sig-default">1</span>)</span><br />
      Return specified diagonals.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#dump" class="summary-sig-name">dump</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">file</span>)</span><br />
      Dump a pickle of the array to the specified file.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#dumps" class="summary-sig-name">dumps</a>(<span class="summary-sig-arg">a</span>)</span><br />
      Returns the pickle of the array as a string.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#fill" class="summary-sig-name">fill</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">value</span>)</span><br />
      Fill the array with a scalar value.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#flatten" class="summary-sig-name">flatten</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">order</span>=<span class="summary-sig-default">'C'</span>)</span><br />
      Return a copy of the array collapsed into one dimension.</td>
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      </table>
      
    </td>
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<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#getfield" class="summary-sig-name">getfield</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">dtype</span>,
        <span class="summary-sig-arg">offset</span>)</span><br />
      Returns a field of the given array as a certain type.</td>
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      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#item" class="summary-sig-name">item</a>(<span class="summary-sig-arg">a</span>)</span><br />
      Copy the first element of array to a standard Python scalar and return
it.</td>
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        </tr>
      </table>
      
    </td>
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<tr>
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    </td><td class="summary">
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        <tr>
          <td><span class="summary-sig"><a name="itemset"></a><span class="summary-sig-name">itemset</span>(<span class="summary-sig-arg">...</span>)</span></td>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#max" class="summary-sig-name">max</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Return the maximum along a given axis.</td>
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    </td>
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<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#mean" class="summary-sig-name">mean</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
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        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Returns the average of the array elements along given axis.</td>
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<tr>
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      <table width="100%" cellpadding="0" cellspacing="0" border="0">
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          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#min" class="summary-sig-name">min</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Return the minimum along a given axis.</td>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#newbyteorder" class="summary-sig-name">newbyteorder</a>(<span class="summary-sig-arg">arr</span>,
        <span class="summary-sig-arg">new_order</span>=<span class="summary-sig-default">'S'</span>)</span><br />
      Return the array with the same data viewed with a different byte order.</td>
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        </tr>
      </table>
      
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<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#nonzero" class="summary-sig-name">nonzero</a>(<span class="summary-sig-arg">a</span>)</span><br />
      Return the indices of the elements that are non-zero.</td>
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    </td>
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<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
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          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#prod" class="summary-sig-name">prod</a>(<span class="summary-sig-arg">a</span>,
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        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Return the product of the array elements over the given axis</td>
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    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#ptp" class="summary-sig-name">ptp</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Peak to peak (maximum - minimum) value along a given axis.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#put" class="summary-sig-name">put</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">indices</span>,
        <span class="summary-sig-arg">values</span>,
        <span class="summary-sig-arg">mode</span>=<span class="summary-sig-default">'raise'</span>)</span><br />
      Set a.flat[n] = values[n] for all n in indices.</td>
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          </td>
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    </td>
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<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#ravel" class="summary-sig-name">ravel</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">order</span>=<span class="summary-sig-default">...</span>)</span><br />
      Return a flattened array.</td>
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          </td>
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      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#repeat" class="summary-sig-name">repeat</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">repeats</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>)</span><br />
      Repeat elements of an array.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#reshape" class="summary-sig-name">reshape</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">shape</span>,
        <span class="summary-sig-arg">order</span>=<span class="summary-sig-default">'C'</span>)</span><br />
      Returns an array containing the same data with a new shape.</td>
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        </tr>
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    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#resize" class="summary-sig-name">resize</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">new_shape</span>,
        <span class="summary-sig-arg">refcheck</span>=<span class="summary-sig-default">True</span>,
        <span class="summary-sig-arg">order</span>=<span class="summary-sig-default">False</span>)</span><br />
      Change shape and size of array in-place.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#round" class="summary-sig-name">round</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">decimals</span>=<span class="summary-sig-default">0</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Return an array rounded a to the given number of decimals.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#searchsorted" class="summary-sig-name">searchsorted</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">v</span>,
        <span class="summary-sig-arg">side</span>=<span class="summary-sig-default">'left'</span>)</span><br />
      Find indices where elements of v should be inserted in a to maintain order.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type"><pre class="literalblock">
None

</pre></span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="setfield"></a><span class="summary-sig-name">setfield</span>(<span class="summary-sig-arg">m</span>,
        <span class="summary-sig-arg">value</span>,
        <span class="summary-sig-arg">dtype</span>,
        <span class="summary-sig-arg">offset</span>)</span><br />
      places val into field of the given array defined by the data type and offset.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a name="setflags"></a><span class="summary-sig-name">setflags</span>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">write</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">align</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">uic</span>=<span class="summary-sig-default">None</span>)</span></td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#sort" class="summary-sig-name">sort</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">-1</span>,
        <span class="summary-sig-arg">kind</span>=<span class="summary-sig-default">'quicksort'</span>,
        <span class="summary-sig-arg">order</span>=<span class="summary-sig-default">None</span>)</span><br />
      Sort an array, in-place.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#squeeze" class="summary-sig-name">squeeze</a>(<span class="summary-sig-arg">a</span>)</span><br />
      Remove single-dimensional entries from the shape of `a`.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#std" class="summary-sig-name">std</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">dtype</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">ddof</span>=<span class="summary-sig-default">0</span>)</span><br />
      Returns the standard deviation of the array elements along given axis.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#sum" class="summary-sig-name">sum</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">dtype</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Return the sum of the array elements over the given axis.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#swapaxes" class="summary-sig-name">swapaxes</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis1</span>,
        <span class="summary-sig-arg">axis2</span>)</span><br />
      Return a view of the array with `axis1` and `axis2` interchanged.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#take" class="summary-sig-name">take</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">indices</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">mode</span>=<span class="summary-sig-default">'raise'</span>)</span><br />
      Return an array formed from the elements of a at the given indices.</td>
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          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#tofile" class="summary-sig-name">tofile</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">fid</span>,
        <span class="summary-sig-arg">sep</span>=<span class="summary-sig-default">&quot;&quot;</span>,
        <span class="summary-sig-arg">format</span>=<span class="summary-sig-default">&quot;%s&quot;</span>)</span><br />
      Write array to a file as text or binary.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#tolist" class="summary-sig-name">tolist</a>(<span class="summary-sig-arg">a</span>)</span><br />
      Return the array as a possibly nested list.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#tostring" class="summary-sig-name">tostring</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">order</span>=<span class="summary-sig-default">'C'</span>)</span><br />
      Construct a Python string containing the raw data bytes in the array.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#trace" class="summary-sig-name">trace</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">offset</span>=<span class="summary-sig-default">0</span>,
        <span class="summary-sig-arg">axis1</span>=<span class="summary-sig-default">0</span>,
        <span class="summary-sig-arg">axis2</span>=<span class="summary-sig-default">1</span>,
        <span class="summary-sig-arg">dtype</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>)</span><br />
      Return the sum along diagonals of the array.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#transpose" class="summary-sig-name">transpose</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">*axes</span>)</span><br />
      Returns a view of 'a' with axes transposed.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#var" class="summary-sig-name">var</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">axis</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">dtype</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">out</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">ddof</span>=<span class="summary-sig-default">0</span>)</span><br />
      Returns the variance of the array elements, along given axis.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
      <table width="100%" cellpadding="0" cellspacing="0" border="0">
        <tr>
          <td><span class="summary-sig"><a href="numpy.ndarray-class.html#view" class="summary-sig-name">view</a>(<span class="summary-sig-arg">a</span>,
        <span class="summary-sig-arg">dtype</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">type</span>=<span class="summary-sig-default">None</span>)</span><br />
      New view of array with the same data.</td>
          <td align="right" valign="top">
            
            
          </td>
        </tr>
      </table>
      
    </td>
  </tr>
  <tr>
    <td colspan="2" class="summary">
    <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
      <code>__delattr__</code>,
      <code>__format__</code>,
      <code>__getattribute__</code>,
      <code>__hash__</code>,
      <code>__init__</code>,
      <code>__reduce_ex__</code>,
      <code>__setattr__</code>,
      <code>__sizeof__</code>,
      <code>__subclasshook__</code>
      </p>
    </td>
  </tr>
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      <span class="summary-type">&nbsp;</span>
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        <a href="numpy.ndarray-class.html#T" class="summary-name">T</a><br />
      Same as self.transpose() except self is returned for self.ndim &lt; 2.
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      None.
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      Array protocol: Python side.
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
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      Array priority.
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      Array protocol: C-struct side.
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      <span class="summary-type">&nbsp;</span>
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        <a href="numpy.ndarray-class.html#base" class="summary-name">base</a><br />
      Base object if memory is from some other object.
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
        <a name="ctypes"></a><span class="summary-name">ctypes</span><br />
      A ctypes interface object.
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      <span class="summary-type">&nbsp;</span>
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      Buffer object pointing to the start of the data.
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      Data-type for the array.
    </td>
  </tr>
<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
        <a href="numpy.ndarray-class.html#flags" class="summary-name">flags</a><br />
      Information about the memory layout of the array.
    </td>
  </tr>
<tr>
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      <span class="summary-type">&nbsp;</span>
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        <a href="numpy.ndarray-class.html#flat" class="summary-name">flat</a><br />
      A 1-D flat iterator over the array.
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
        <a href="numpy.ndarray-class.html#imag" class="summary-name">imag</a><br />
      The imaginary part of the array.
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
        <a href="numpy.ndarray-class.html#itemsize" class="summary-name">itemsize</a><br />
      Length of one element in bytes.
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<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
        <a href="numpy.ndarray-class.html#nbytes" class="summary-name">nbytes</a><br />
      Number of bytes in the array.
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
        <a href="numpy.ndarray-class.html#ndim" class="summary-name">ndim</a><br />
      Number of array dimensions.
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
        <a href="numpy.ndarray-class.html#real" class="summary-name">real</a><br />
      The real part of the array.
    </td>
  </tr>
<tr>
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      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
        <a href="numpy.ndarray-class.html#shape" class="summary-name">shape</a><br />
      Tuple of array dimensions.
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
        <a href="numpy.ndarray-class.html#size" class="summary-name">size</a><br />
      Number of elements in the array.
    </td>
  </tr>
<tr>
    <td width="15%" align="right" valign="top" class="summary">
      <span class="summary-type">&nbsp;</span>
    </td><td class="summary">
        <a href="numpy.ndarray-class.html#strides" class="summary-name">strides</a><br />
      Tuple of bytes to step in each dimension.
    </td>
  </tr>
  <tr>
    <td colspan="2" class="summary">
    <p class="indent-wrapped-lines"><b>Inherited from <code>object</code></b>:
      <code>__class__</code>
      </p>
    </td>
  </tr>
</table>
<!-- ==================== METHOD DETAILS ==================== -->
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<a name="__array__"></a>
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  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
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  <pre class="literalblock">
a.__array__(|dtype) -&gt; reference if type unchanged, copy otherwise.

Returns either a new reference to self if dtype is not given or a new array
of provided data type if dtype is different from the current dtype of the
array.

</pre>
  <dl class="fields">
  </dl>
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<a name="__copy__"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">__copy__</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">order</span>=<span class="sig-default">...</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
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  <pre class="literalblock">
Return a copy of the array.

Parameters
----------
order : {'C', 'F', 'A'}, optional
    If order is 'C' (False) then the result is contiguous (default).
    If order is 'Fortran' (True) then the result has fortran order.
    If order is 'Any' (None) then the result has fortran order
    only if the array already is in fortran order.

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  <h3 class="epydoc"><span class="sig"><span class="sig-name">__delslice__</span>(<span class="sig-arg">x</span>,
        <span class="sig-arg">i</span>,
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    <br /><em class="fname">(Slice deletion operator)</em>
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del x[i:j]

Use of negative indices is not supported.

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<a name="__getslice__"></a>
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        <span class="sig-arg">i</span>,
        <span class="sig-arg">j</span>)</span>
    <br /><em class="fname">(Slicling operator)</em>
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  <pre class="literalblock">
x[i:j]

Use of negative indices is not supported.

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    >&nbsp;
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  <pre class="literalblock">


</pre>
  <dl class="fields">
    <dt>Returns: <pre class="literalblock">
a new object with type S, a subtype of T

</pre></dt>
    <dt>Overrides:
        object.__new__
    </dt>
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<a name="__reduce__"></a>
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  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
For pickling.

</pre>
  <dl class="fields">
    <dt>Overrides:
        object.__reduce__
    </dt>
  </dl>
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</div>
<a name="__repr__"></a>
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  </h3>
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    >&nbsp;
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  <pre class="literalblock">
repr(x)

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        <span class="sig-arg">i</span>,
        <span class="sig-arg">j</span>,
        <span class="sig-arg">y</span>)</span>
    <br /><em class="fname">(Slice assignment operator)</em>
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    >&nbsp;
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  <pre class="literalblock">
x[i:j]=y

Use  of negative indices is not supported.

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<a name="__setstate__"></a>
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        <span class="sig-arg">version</span>,
        <span class="sig-arg">shape</span>,
        <span class="sig-arg">dtype</span>,
        <span class="sig-arg">isfortran</span>,
        <span class="sig-arg">rawdata</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
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  <pre class="literalblock">
For unpickling.

Parameters
----------
version : int
    optional pickle version. If omitted defaults to 0.
shape : tuple
dtype : data-type
isFortran : bool
rawdata : string or list
    a binary string with the data (or a list if 'a' is an object array)

</pre>
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<a name="__str__"></a>
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    <br /><em class="fname">(Informal representation operator)</em>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
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  <pre class="literalblock">
str(x)

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  <dl class="fields">
    <dt>Overrides:
        object.__str__
    </dt>
  </dl>
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<a name="all"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">all</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Returns True if all elements evaluate to True.

Refer to `numpy.all` for full documentation.

See Also
--------
numpy.all : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
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<a name="any"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">any</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
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  <pre class="literalblock">
Check if any of the elements of `a` are true.

Refer to `numpy.any` for full documentation.

See Also
--------
numpy.any : equivalent function

</pre>
  <dl class="fields">
  </dl>
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<a name="argmax"></a>
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        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return indices of the maximum values along the given axis of `a`.

Parameters
----------
axis : int, optional
    Axis along which to operate.  By default flattened input is used.
out : ndarray, optional
    Alternative output array in which to place the result.  Must
    be of the same shape and buffer length as the expected output.

Returns
-------
index_array : ndarray
    An array of indices or single index value, or a reference to `out`
    if it was specified.

Examples
--------
&gt;&gt;&gt; a = np.arange(6).reshape(2,3)
&gt;&gt;&gt; a.argmax()
5
&gt;&gt;&gt; a.argmax(0)
array([1, 1, 1])
&gt;&gt;&gt; a.argmax(1)
array([2, 2])

</pre>
  <dl class="fields">
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<a name="argmin"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">argmin</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
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  <pre class="literalblock">
Return indices of the minimum values along the given axis of `a`.

Refer to `numpy.ndarray.argmax` for detailed documentation.

</pre>
  <dl class="fields">
  </dl>
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</div>
<a name="argsort"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">argsort</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">-1</span>,
        <span class="sig-arg">kind</span>=<span class="sig-default">'quicksort'</span>,
        <span class="sig-arg">order</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
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  <pre class="literalblock">
Returns the indices that would sort this array.

Refer to `numpy.argsort` for full documentation.

See Also
--------
numpy.argsort : equivalent function

</pre>
  <dl class="fields">
  </dl>
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<a name="astype"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">astype</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">t</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Copy of the array, cast to a specified type.

Parameters
----------
t : string or dtype
    Typecode or data-type to which the array is cast.

Examples
--------
&gt;&gt;&gt; x = np.array([1, 2, 2.5])
&gt;&gt;&gt; x
array([ 1. ,  2. ,  2.5])

&gt;&gt;&gt; x.astype(int)
array([1, 2, 2])

</pre>
  <dl class="fields">
  </dl>
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<a name="byteswap"></a>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">byteswap</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">inplace</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Swap the bytes of the array elements

Toggle between low-endian and big-endian data representation by
returning a byteswapped array, optionally swapped in-place.

Parameters
----------
inplace: bool, optional
    If ``True``, swap bytes in-place, default is ``False``.

Returns
-------
out: ndarray
    The byteswapped array. If `inplace` is ``True``, this is
    a view to self.

Examples
--------
&gt;&gt;&gt; A = np.array([1, 256, 8755], dtype=np.int16)
&gt;&gt;&gt; map(hex, A)
['0x1', '0x100', '0x2233']
&gt;&gt;&gt; A.byteswap(True)
array([  256,     1, 13090], dtype=int16)
&gt;&gt;&gt; map(hex, A)
['0x100', '0x1', '0x3322']

Arrays of strings are not swapped

&gt;&gt;&gt; A = np.array(['ceg', 'fac'])
&gt;&gt;&gt; A.byteswap()
array(['ceg', 'fac'],
      dtype='|S3')

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="choose"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">choose</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">choices</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">mode</span>=<span class="sig-default">'raise'</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Use an index array to construct a new array from a set of choices.

Refer to `numpy.choose` for full documentation.

See Also
--------
numpy.choose : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="clip"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">clip</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">a_min</span>,
        <span class="sig-arg">a_max</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return an array whose values are limited to ``[a_min, a_max]``.

Refer to `numpy.clip` for full documentation.

See Also
--------
numpy.clip : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="compress"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">compress</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">condition</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return selected slices of this array along given axis.

Refer to `numpy.compress` for full documentation.

See Also
--------
numpy.compress : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="copy"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">copy</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">order</span>=<span class="sig-default">'C'</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return a copy of the array.

Parameters
----------
order : {'C', 'F', 'A'}, optional
    By default, the result is stored in C-contiguous (row-major) order in
    memory.  If `order` is `F`, the result has 'Fortran' (column-major)
    order.  If order is 'A' ('Any'), then the result has the same order
    as the input.

Examples
--------
&gt;&gt;&gt; x = np.array([[1,2,3],[4,5,6]], order='F')

&gt;&gt;&gt; y = x.copy()

&gt;&gt;&gt; x.fill(0)

&gt;&gt;&gt; x
array([[0, 0, 0],
       [0, 0, 0]])

&gt;&gt;&gt; y
array([[1, 2, 3],
       [4, 5, 6]])

&gt;&gt;&gt; y.flags['C_CONTIGUOUS']
True

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="cumprod"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">cumprod</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">dtype</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return the cumulative product of the elements along the given axis.

Refer to `numpy.cumprod` for full documentation.

See Also
--------
numpy.cumprod : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="cumsum"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">cumsum</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">dtype</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return the cumulative sum of the elements along the given axis.

Refer to `numpy.cumsum` for full documentation.

See Also
--------
numpy.cumsum : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="diagonal"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">diagonal</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">offset</span>=<span class="sig-default">0</span>,
        <span class="sig-arg">axis1</span>=<span class="sig-default">0</span>,
        <span class="sig-arg">axis2</span>=<span class="sig-default">1</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return specified diagonals.

Refer to `numpy.diagonal` for full documentation.

See Also
--------
numpy.diagonal : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="dump"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">dump</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">file</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Dump a pickle of the array to the specified file.
The array can be read back with pickle.load or numpy.load.

Parameters
----------
file : str
    A string naming the dump file.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="dumps"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">dumps</span>(<span class="sig-arg">a</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Returns the pickle of the array as a string.
pickle.loads or numpy.loads will convert the string back to an array.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="fill"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">fill</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">value</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Fill the array with a scalar value.

Parameters
----------
a : ndarray
    Input array
value : scalar
    All elements of `a` will be assigned this value.

Examples
--------
&gt;&gt;&gt; a = np.array([1, 2])
&gt;&gt;&gt; a.fill(0)
&gt;&gt;&gt; a
array([0, 0])
&gt;&gt;&gt; a = np.empty(2)
&gt;&gt;&gt; a.fill(1)
&gt;&gt;&gt; a
array([ 1.,  1.])

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="flatten"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">flatten</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">order</span>=<span class="sig-default">'C'</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return a copy of the array collapsed into one dimension.

Parameters
----------
order : {'C', 'F'}, optional
    Whether to flatten in C (row-major) or Fortran (column-major) order.
    The default is 'C'.

Returns
-------
y : ndarray
    A copy of the input array, flattened to one dimension.

See Also
--------
ravel : Return a flattened array.
flat : A 1-D flat iterator over the array.

Examples
--------
&gt;&gt;&gt; a = np.array([[1,2], [3,4]])
&gt;&gt;&gt; a.flatten()
array([1, 2, 3, 4])
&gt;&gt;&gt; a.flatten('F')
array([1, 3, 2, 4])

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="getfield"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">getfield</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">dtype</span>,
        <span class="sig-arg">offset</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Returns a field of the given array as a certain type. A field is a view of
the array data with each itemsize determined by the given type and the
offset into the current array.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="item"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">item</span>(<span class="sig-arg">a</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Copy the first element of array to a standard Python scalar and return
it. The array must be of size one.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="max"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">max</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return the maximum along a given axis.

Refer to `numpy.amax` for full documentation.

See Also
--------
numpy.amax : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="mean"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">mean</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">dtype</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Returns the average of the array elements along given axis.

Refer to `numpy.mean` for full documentation.

See Also
--------
numpy.mean : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="min"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">min</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return the minimum along a given axis.

Refer to `numpy.amin` for full documentation.

See Also
--------
numpy.amin : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="newbyteorder"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">newbyteorder</span>(<span class="sig-arg">arr</span>,
        <span class="sig-arg">new_order</span>=<span class="sig-default">'S'</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return the array with the same data viewed with a different byte order.

Equivalent to::

    arr.view(arr.dtype.newbytorder(new_order))

Changes are also made in all fields and sub-arrays of the array data
type.



Parameters
----------
new_order : string, optional
    Byte order to force; a value from the byte order specifications
    above. `new_order` codes can be any of::

     * 'S' - swap dtype from current to opposite endian
     * {'&lt;', 'L'} - little endian
     * {'&gt;', 'B'} - big endian
     * {'=', 'N'} - native order
     * {'|', 'I'} - ignore (no change to byte order)

    The default value ('S') results in swapping the current
    byte order. The code does a case-insensitive check on the first
    letter of `new_order` for the alternatives above.  For example,
    any of 'B' or 'b' or 'biggish' are valid to specify big-endian.


Returns
-------
new_arr : array
    New array object with the dtype reflecting given change to the
    byte order.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="nonzero"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">nonzero</span>(<span class="sig-arg">a</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return the indices of the elements that are non-zero.

Refer to `numpy.nonzero` for full documentation.

See Also
--------
numpy.nonzero : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="prod"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">prod</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">dtype</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return the product of the array elements over the given axis

Refer to `numpy.prod` for full documentation.

See Also
--------
numpy.prod : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="ptp"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">ptp</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Peak to peak (maximum - minimum) value along a given axis.

Refer to `numpy.ptp` for full documentation.

See Also
--------
numpy.ptp : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="put"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">put</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">indices</span>,
        <span class="sig-arg">values</span>,
        <span class="sig-arg">mode</span>=<span class="sig-default">'raise'</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Set a.flat[n] = values[n] for all n in indices.

Refer to `numpy.put` for full documentation.

See Also
--------
numpy.put : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="ravel"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">ravel</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">order</span>=<span class="sig-default">...</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return a flattened array.

Refer to `numpy.ravel` for full documentation.

See Also
--------
numpy.ravel : equivalent function

ndarray.flat : a flat iterator on the array.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="repeat"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">repeat</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">repeats</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Repeat elements of an array.

Refer to `numpy.repeat` for full documentation.

See Also
--------
numpy.repeat : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="reshape"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">reshape</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">shape</span>,
        <span class="sig-arg">order</span>=<span class="sig-default">'C'</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Returns an array containing the same data with a new shape.

Refer to `numpy.reshape` for full documentation.

See Also
--------
numpy.reshape : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="resize"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">resize</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">new_shape</span>,
        <span class="sig-arg">refcheck</span>=<span class="sig-default">True</span>,
        <span class="sig-arg">order</span>=<span class="sig-default">False</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Change shape and size of array in-place.

Parameters
----------
a : ndarray
    Input array.
new_shape : {tuple, int}
    Shape of resized array.
refcheck : bool, optional
    If False, memory referencing will not be checked. Default is True.
order : bool, optional
    &lt;needs an explanation&gt;. Default if False.

Returns
-------
None

Raises
------
ValueError
    If `a` does not own its own data, or references or views to it exist.

Examples
--------
Shrinking an array: array is flattened in C-order, resized, and reshaped:

&gt;&gt;&gt; a = np.array([[0,1],[2,3]])
&gt;&gt;&gt; a.resize((2,1))
&gt;&gt;&gt; a
array([[0],
       [1]])

Enlarging an array: as above, but missing entries are filled with zeros:

&gt;&gt;&gt; b = np.array([[0,1],[2,3]])
&gt;&gt;&gt; b.resize((2,3))
&gt;&gt;&gt; b
array([[0, 1, 2],
       [3, 0, 0]])

Referencing an array prevents resizing:

&gt;&gt;&gt; c = a
&gt;&gt;&gt; a.resize((1,1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that has been referenced ...

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="round"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">round</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">decimals</span>=<span class="sig-default">0</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return an array rounded a to the given number of decimals.

Refer to `numpy.around` for full documentation.

See Also
--------
numpy.around : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="searchsorted"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">searchsorted</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">v</span>,
        <span class="sig-arg">side</span>=<span class="sig-default">'left'</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Find indices where elements of v should be inserted in a to maintain order.

For full documentation, see `numpy.searchsorted`

See Also
--------
numpy.searchsorted : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="sort"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">sort</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">-1</span>,
        <span class="sig-arg">kind</span>=<span class="sig-default">'quicksort'</span>,
        <span class="sig-arg">order</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Sort an array, in-place.

Parameters
----------
axis : int, optional
    Axis along which to sort. Default is -1, which means sort along the
    last axis.
kind : {'quicksort', 'mergesort', 'heapsort'}, optional
    Sorting algorithm. Default is 'quicksort'.
order : list, optional
    When `a` is an array with fields defined, this argument specifies
    which fields to compare first, second, etc.  Not all fields need be
    specified.

See Also
--------
numpy.sort : Return a sorted copy of an array.
argsort : Indirect sort.
lexsort : Indirect stable sort on multiple keys.
searchsorted : Find elements in sorted array.

Notes
-----
See ``sort`` for notes on the different sorting algorithms.

Examples
--------
&gt;&gt;&gt; a = np.array([[1,4], [3,1]])
&gt;&gt;&gt; a.sort(axis=1)
&gt;&gt;&gt; a
array([[1, 4],
       [1, 3]])
&gt;&gt;&gt; a.sort(axis=0)
&gt;&gt;&gt; a
array([[1, 3],
       [1, 4]])

Use the `order` keyword to specify a field to use when sorting a
structured array:

&gt;&gt;&gt; a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
&gt;&gt;&gt; a.sort(order='y')
&gt;&gt;&gt; a
array([('c', 1), ('a', 2)],
      dtype=[('x', '|S1'), ('y', '&lt;i4')])

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="squeeze"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">squeeze</span>(<span class="sig-arg">a</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Remove single-dimensional entries from the shape of `a`.

Refer to `numpy.squeeze` for full documentation.

See Also
--------
numpy.squeeze : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="std"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">std</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">dtype</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">ddof</span>=<span class="sig-default">0</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Returns the standard deviation of the array elements along given axis.

Refer to `numpy.std` for full documentation.

See Also
--------
numpy.std : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="sum"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">sum</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">dtype</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return the sum of the array elements over the given axis.

Refer to `numpy.sum` for full documentation.

See Also
--------
numpy.sum : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="swapaxes"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">swapaxes</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis1</span>,
        <span class="sig-arg">axis2</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return a view of the array with `axis1` and `axis2` interchanged.

Refer to `numpy.swapaxes` for full documentation.

See Also
--------
numpy.swapaxes : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="take"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">take</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">indices</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">mode</span>=<span class="sig-default">'raise'</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return an array formed from the elements of a at the given indices.

Refer to `numpy.take` for full documentation.

See Also
--------
numpy.take : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="tofile"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">tofile</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">fid</span>,
        <span class="sig-arg">sep</span>=<span class="sig-default">&quot;&quot;</span>,
        <span class="sig-arg">format</span>=<span class="sig-default">&quot;%s&quot;</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Write array to a file as text or binary.

Data is always written in 'C' order, independently of the order of `a`.
The data produced by this method can be recovered by using the function
fromfile().

This is a convenience function for quick storage of array data.
Information on endianess and precision is lost, so this method is not a
good choice for files intended to archive data or transport data between
machines with different endianess. Some of these problems can be overcome
by outputting the data as text files at the expense of speed and file size.

Parameters
----------
fid : file or string
    An open file object or a string containing a filename.
sep : string
    Separator between array items for text output.
    If &quot;&quot; (empty), a binary file is written, equivalently to
    file.write(a.tostring()).
format : string
    Format string for text file output.
    Each entry in the array is formatted to text by converting it to the
    closest Python type, and using &quot;format&quot; % item.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="tolist"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">tolist</span>(<span class="sig-arg">a</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return the array as a possibly nested list.

Return a copy of the array data as a (nested) Python list.
Data items are converted to the nearest compatible Python type.

Parameters
----------
none

Returns
-------
y : list
    The possibly nested list of array elements.

Notes
-----
The array may be recreated, ``a = np.array(a.tolist())``.

Examples
--------
&gt;&gt;&gt; a = np.array([1, 2])
&gt;&gt;&gt; a.tolist()
[1, 2]
&gt;&gt;&gt; a = np.array([[1, 2], [3, 4]])
&gt;&gt;&gt; list(a)
[array([1, 2]), array([3, 4])]
&gt;&gt;&gt; a.tolist()
[[1, 2], [3, 4]]

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="tostring"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">tostring</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">order</span>=<span class="sig-default">'C'</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Construct a Python string containing the raw data bytes in the array.

Parameters
----------
order : {'C', 'F', None}
    Order of the data for multidimensional arrays:
    C, Fortran, or the same as for the original array.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="trace"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">trace</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">offset</span>=<span class="sig-default">0</span>,
        <span class="sig-arg">axis1</span>=<span class="sig-default">0</span>,
        <span class="sig-arg">axis2</span>=<span class="sig-default">1</span>,
        <span class="sig-arg">dtype</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Return the sum along diagonals of the array.

Refer to `numpy.trace` for full documentation.

See Also
--------
numpy.trace : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="transpose"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">transpose</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">*axes</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Returns a view of 'a' with axes transposed. If no axes are given,
or None is passed, switches the order of the axes. For a 2-d
array, this is the usual matrix transpose. If axes are given,
they describe how the axes are permuted.

See Also
--------
ndarray.T : array property returning the array transposed


Examples
--------
&gt;&gt;&gt; a = np.array([[1,2],[3,4]])
&gt;&gt;&gt; a
array([[1, 2],
       [3, 4]])
&gt;&gt;&gt; a.transpose()
array([[1, 3],
       [2, 4]])
&gt;&gt;&gt; a.transpose((1,0))
array([[1, 3],
       [2, 4]])
&gt;&gt;&gt; a.transpose(1,0)
array([[1, 3],
       [2, 4]])

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="var"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">var</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">axis</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">dtype</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">out</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">ddof</span>=<span class="sig-default">0</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
Returns the variance of the array elements, along given axis.

Refer to `numpy.var` for full documentation.

See Also
--------
numpy.var : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="view"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">view</span>(<span class="sig-arg">a</span>,
        <span class="sig-arg">dtype</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">type</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    >&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">
New view of array with the same data.

Parameters
----------
dtype : data-type
    Data-type descriptor of the returned view, e.g. float32 or int16.
type : python type
    Type of the returned view, e.g. ndarray or matrix.

Examples
--------
&gt;&gt;&gt; x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])

Viewing array data using a different type and dtype:

&gt;&gt;&gt; y = x.view(dtype=np.int16, type=np.matrix)
&gt;&gt;&gt; print y.dtype
int16

&gt;&gt;&gt; print type(y)
&lt;class 'numpy.core.defmatrix.matrix'&gt;

Using a view to convert an array to a record array:

&gt;&gt;&gt; z = x.view(np.recarray)
&gt;&gt;&gt; z.a
array([1], dtype=int8)

Views share data:

&gt;&gt;&gt; x[0] = (9, 10)
&gt;&gt;&gt; z[0]
(9, 10)

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<br />
<!-- ==================== PROPERTY DETAILS ==================== -->
<a name="section-PropertyDetails"></a>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr bgcolor="#70b0f0" class="table-header">
  <td colspan="2" class="table-header">
    <table border="0" cellpadding="0" cellspacing="0" width="100%">
      <tr valign="top">
        <td align="left"><span class="table-header">Property Details</span></td>
        <td align="right" valign="top"
         ><span class="options">[<a href="#section-PropertyDetails"
         class="privatelink" onclick="toggle_private();"
         >hide private</a>]</span></td>
      </tr>
    </table>
  </td>
</tr>
</table>
<a name="T"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">T</h3>
  <pre class="literalblock">
Same as self.transpose() except self is returned for self.ndim &lt; 2.

Examples
--------
&gt;&gt;&gt; x = np.array([[1.,2.],[3.,4.]])
&gt;&gt;&gt; x.T
array([[ 1.,  3.],
       [ 2.,  4.]])

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="base"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">base</h3>
  <pre class="literalblock">
Base object if memory is from some other object.

Examples
--------

Base of an array owning its memory is None:

&gt;&gt;&gt; x = np.array([1,2,3,4])
&gt;&gt;&gt; x.base is None
True

Slicing creates a view, and the memory is shared with x:

&gt;&gt;&gt; y = x[2:]
&gt;&gt;&gt; y.base is x
True

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="flags"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">flags</h3>
  <pre class="literalblock">
Information about the memory layout of the array.

Attributes
----------
C_CONTIGUOUS (C)
    The data is in a single, C-style contiguous segment.
F_CONTIGUOUS (F)
    The data is in a single, Fortran-style contiguous segment.
OWNDATA (O)
    The array owns the memory it uses or borrows it from another object.
WRITEABLE (W)
    The data area can be written to.
ALIGNED (A)
    The data and strides are aligned appropriately for the hardware.
UPDATEIFCOPY (U)
    This array is a copy of some other array. When this array is
    deallocated, the base array will be updated with the contents of
    this array.

FNC
    F_CONTIGUOUS and not C_CONTIGUOUS.
FORC
    F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).
BEHAVED (B)
    ALIGNED and WRITEABLE.
CARRAY (CA)
    BEHAVED and C_CONTIGUOUS.
FARRAY (FA)
    BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.

Notes
-----
The `flags` object can be also accessed dictionary-like, and using
lowercased attribute names. Short flag names are only supported in
dictionary access.

Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by
the user, via assigning to ``flags['FLAGNAME']`` or `ndarray.setflags`.
The array flags cannot be set arbitrarily:

- UPDATEIFCOPY can only be set ``False``.
- ALIGNED can only be set ``True`` if the data is truly aligned.
- WRITEABLE can only be set ``True`` if the array owns its own memory
  or the ultimate owner of the memory exposes a writeable buffer
  interface or is a string.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="flat"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">flat</h3>
  <pre class="literalblock">
A 1-D flat iterator over the array.

This is a `flatiter` instance, which acts similarly to a Python iterator.

See Also
--------
flatten : Return a copy of the array collapsed into one dimension.
flatiter

Examples
--------
&gt;&gt;&gt; x = np.arange(1, 7).reshape(2, 3)
&gt;&gt;&gt; x
array([[1, 2, 3],
       [4, 5, 6]])
&gt;&gt;&gt; x.flat[3]
4
&gt;&gt;&gt; x.T
array([[1, 4],
       [2, 5],
       [3, 6]])
&gt;&gt;&gt; x.T.flat[3]
5

&gt;&gt;&gt; type(x.flat)
&lt;type 'numpy.flatiter'&gt;

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="imag"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">imag</h3>
  <pre class="literalblock">
The imaginary part of the array.

Examples
--------
&gt;&gt;&gt; x = np.sqrt([1+0j, 0+1j])
&gt;&gt;&gt; x.imag
array([ 0.        ,  0.70710678])
&gt;&gt;&gt; x.imag.dtype
dtype('float64')

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="itemsize"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">itemsize</h3>
  <pre class="literalblock">
Length of one element in bytes.

Examples
--------
&gt;&gt;&gt; x = np.array([1,2,3], dtype=np.float64)
&gt;&gt;&gt; x.itemsize
8
&gt;&gt;&gt; x = np.array([1,2,3], dtype=np.complex128)
&gt;&gt;&gt; x.itemsize
16

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="nbytes"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">nbytes</h3>
  <pre class="literalblock">
Number of bytes in the array.

Examples
--------
&gt;&gt;&gt; x = np.zeros((3,5,2), dtype=np.complex128)
&gt;&gt;&gt; x.nbytes
480
&gt;&gt;&gt; np.prod(x.shape) * x.itemsize
480

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="ndim"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">ndim</h3>
  <pre class="literalblock">
Number of array dimensions.

Examples
--------

&gt;&gt;&gt; x = np.array([1,2,3])
&gt;&gt;&gt; x.ndim
1
&gt;&gt;&gt; y = np.zeros((2,3,4))
&gt;&gt;&gt; y.ndim
3

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="real"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">real</h3>
  <pre class="literalblock">
The real part of the array.

Examples
--------
&gt;&gt;&gt; x = np.sqrt([1+0j, 0+1j])
&gt;&gt;&gt; x.real
array([ 1.        ,  0.70710678])
&gt;&gt;&gt; x.real.dtype
dtype('float64')

See Also
--------
numpy.real : equivalent function

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="shape"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">shape</h3>
  <pre class="literalblock">
Tuple of array dimensions.

Examples
--------
&gt;&gt;&gt; x = np.array([1,2,3,4])
&gt;&gt;&gt; x.shape
(4,)
&gt;&gt;&gt; y = np.zeros((4,5,6))
&gt;&gt;&gt; y.shape
(4, 5, 6)
&gt;&gt;&gt; y.shape = (2, 5, 2, 3, 2)
&gt;&gt;&gt; y.shape
(2, 5, 2, 3, 2)

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="size"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">size</h3>
  <pre class="literalblock">
Number of elements in the array.

Examples
--------
&gt;&gt;&gt; x = np.zeros((3,5,2), dtype=np.complex128)
&gt;&gt;&gt; x.size
30

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="strides"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">strides</h3>
  <pre class="literalblock">
Tuple of bytes to step in each dimension.

The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`
is::

    offset = sum(np.array(i) * a.strides)

Examples
--------
&gt;&gt;&gt; x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
&gt;&gt;&gt; x.strides
(32, 4, 224, 1344)
&gt;&gt;&gt; i = np.array([3,5,2,2])
&gt;&gt;&gt; offset = sum(i * x.strides)
&gt;&gt;&gt; x[3,5,2,2]
813
&gt;&gt;&gt; offset / x.itemsize
813

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
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